Building an AI-Native Foundation for Scalable Growth for a Big 4 Consulting Firm


Building an AI-Native Foundation for Scalable Growth for a Big 4 Consulting Firm


A global big 4 consulting firm sought to establish a scalable, AI-native foundation to enhance operations, accelerate innovation, and deliver superior client outcomes. Galent designed and implemented a unified AI architecture, streamlined model lifecycle management, and robust data governance solutions.

The result – a scalable, efficient AI ecosystem that reduced time-to-market, improved decision-making with real-time insights, and positioned the firm as a pioneer in AI-driven consulting.

Client Challenges:

1. Fragmented AI Infrastructure: Existing tools and processes lacked cohesion, resulting in slower innovation cycles.

2. Scalability Roadblocks: Rapidly growing client needs demanded a scalable AI architecture to handle diverse use cases.

3. Data Management Complexity: Managing vast amounts of data across geographies and industries while maintaining accuracy and compliance.

4. Operational Inefficiency: Disjointed AI model management hindered productivity and operational excellence.

Strategic Interventions

  • AI-Native Reference Architecture Design: We created a unified and modular AI-native architecture to enable seamless integration across tools, teams, and processes.

    • Outcome:Established a flexible framework capable of supporting current and future AI deployments.

  • RAG (Retrieval-Augmented Generation) Architecture Implementation:We designed and implemented a RAG-based system for rapid, context-aware knowledge retrieval.

    • Outcome:Accelerated decision-making for client-facing teams by delivering real-time insights from unstructured data sources.

  • AI Model Lifecycle Management: We also deployed end-to-end model lifecycle tools to streamline the development, testing, and deployment of AI models.

    • Outcome:Reduced time-to-market for AI-powered solutions and enhanced model performance tracking.

  • AIOps Integration:We automated monitoring and management of AI systems with AIOps, ensuring optimal performance and reducing downtime.

    • Outcome: Achieved operational efficiency by minimizing system disruptions and enhancing model reliability.

  • AI Data Management and Agentic Workbench: We implemented a centralized platform for AI data governance and an agentic workbench for experimentation.

    • Outcome: Improved compliance, data accuracy, and facilitated innovation through a collaborative development environment.

Business Outcomes

  • Delivered a scalable AI ecosystem capable of handling increasing client demands and complex use cases.

  • Achieved significant efficiency improvements through automation and streamlined workflows.

  • Accelerated the deployment of AI models, enabling quicker time-to-market for solutions.

  • Enabled client-facing teams to make informed decisions backed by real-time, AI-generated insights.

  • Elevated the firm’s reputation as an AI leader by delivering innovative and impactful solutions.

Executive Insight: A Client Perspective

A strategic view on collaboration, innovation, and measurable outcomes.

“Galent’s expertise in AI-native services enabled us to reimagine our operations and deliver unparalleled value to our clients. This partnership has been a cornerstone of our transformation journey.”


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